Lower limb muscle fatigue has been evaluated in previous studies to understand painrelated movement variability by analyzing different muscles using surface electromyography (sEMG) and angular position signals; however, further studies are needed to particularly understand strength loss due to gait and to inform the development of intelligent control systems for rehabilitation devices in the prevention and management of musculoskeletal or balance control disorders in the Latin American population. A pilot study was developed to characterize muscle fatigue using a walking fatigue detection (WFD) protocol, an instrumented orthosis and a treadmill. Electrical activity was acquired from Rectus Femoris (RF), Biceps Femoris (BF), Tibialis Anterior (TA) and Gastrocnemius Lateralis (GL) muscles, as well as the angular position of the hip and knee of sixteen healthy Latin-American women, aged 22–34 years, 63.5 ± 6 kg mass, and 161 ± 7 cm height. Data were analyzed with a one-way ANOVA analysis of variance and Tukey’s test. Preliminary results show that muscle fatigue is clearly identifiable and is represented by a decrease in both amplitude and frequency of the sEMG signal and lower limb angular position. Muscle fatigue was evident in 93.75% of the participants at the end of the test. 75% of the participants experienced muscle fatigue halfway through the test, of which 31.35% were unable to regain strength causing more muscles to fatigue, due to the extra effort they were enduring it was also found that when one muscle goes into fatigue, another muscle supports the action observing muscle compensation but without a uniform pattern.
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Load distribution analysis on foot surface allows knowing human mechanical behavior and aids the doctor in the detection of gait disorders like, the risk of foot ulcerations, leg discrepancy, and footprint alterations. Plantar pressure data combined with techniques that use integral reasoning produce easy understanding medical tools for assisting in treatment, early detection, and the development of preventive strategies. The present research compares the classification of human plantar foot alterations using Fuzzy Cognitive Maps (FCM) trained by Genetic Algorithm (GA) against a Multi-Layer Perceptron Neural Network (MLPNN). One hundred and fifty-one subject volunteers (aged 7–77) were classified previously with the flat foot (n = 70) and cavus foot (n = 81) by specialized physicians of the Piédica diagnostic center. The trial walking was conducted using plantar pressure platforms FreeMed®. The foot surface was divided into 14 areas that included toe 1 st to 5th, metatarsal joint 1st to 5th, lateral midfoot, medial midfoot, lateral heel, and medial heel. Pressure data were normalized for each area. Better performance in the classification using small amounts of data were found by using Fuzzy rather than non-Fuzzy approach.
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